Python:计算机视觉实现视频的AI换脸（最基础）

一、实验要求

1、手动点击关键点进行替换

2、利用光流对相邻的视频帧进行关键点的追踪

二、实验结果

三、实验代码

1、手动进行图像关键点的点击

import cv2
import numpy as np# 导入图片
img1 = cv2.imread('path1')
img2 = cv2.imread('path2')a =[]
b = []def on_EVENT_LBUTTONDOWN1(event, x, y, flags, param):# 点击鼠标左键if event == cv2.EVENT_LBUTTONDOWN:xy = "%d,%d" % (x, y)a.append(x)b.append(y)cv2.circle(img1, (x, y), 1, (255, 0, 0), thickness=-1)cv2.putText(img1, xy, (x, y), cv2.FONT_HERSHEY_PLAIN,1.0, (0, 0, 0), thickness=1)cv2.imshow("image1", img1)def on_EVENT_LBUTTONDOWN2(event, x, y, flags, param):# 点击鼠标左键if event == cv2.EVENT_LBUTTONDOWN:xy = "%d,%d" % (x, y)a.append(x)b.append(y)cv2.circle(img2, (x, y), 1, (255, 0, 0), thickness=-1)cv2.putText(img2, xy, (x, y), cv2.FONT_HERSHEY_PLAIN,1.0, (0, 0, 0), thickness=1)cv2.imshow("image2", img2)cv2.namedWindow("image1")
cv2.namedWindow("image2")
cv2.setMouseCallback("image1", on_EVENT_LBUTTONDOWN1)
cv2.setMouseCallback("image2", on_EVENT_LBUTTONDOWN2)
cv2.imshow("image1", img1)
cv2.imshow("image2", img2)
cv2.waitKey(0)cv2.destroyAllWindows()#for i in range(8):
#    print([a[i],b[i]])

调用cv2库和numpy库，利用cv2.setMouseCallback函数得到鼠标的点击信息，并在on_EVENT_LBUTTONDOWN函数中对点击的点进行标记，将点击点的坐标存储在数组a,b里面。

2、利用光流对相邻的视频帧进行关键点的追踪

（1）首先需要提取视频的第一帧，手动进行关键点的标注，调用cap.read函数，此时frame为视频第一帧的二进制流数据

import cv2
cap = cv2.VideoCapture('2.mp4')
ret, frame = cap.read()      #frame是每一帧的图像
scaling_factor = 0.5
frame = cv2.resize(frame, None, fx=scaling_factor,fy=scaling_factor, interpolation=cv2.INTER_AREA)

（2）手动点击关键点

a =[]
b = []def on_EVENT_LBUTTONDOWN1(event, x, y, flags, param):# 点击鼠标左键if event == cv2.EVENT_LBUTTONDOWN:xy = "%d,%d" % (x, y)a.append(x)b.append(y)cv2.circle(frame, (x, y), 1, (255, 0, 0), thickness=-1)cv2.putText(frame, xy, (x, y), cv2.FONT_HERSHEY_PLAIN,1.0, (0, 0, 0), thickness=1)cv2.imshow("image1", frame)def on_EVENT_LBUTTONDOWN2(event, x, y, flags, param):# 点击鼠标左键if event == cv2.EVENT_LBUTTONDOWN:xy = "%d,%d" % (x, y)a.append(x)b.append(y)cv2.circle(img1, (x, y), 1, (255, 0, 0), thickness=-1)cv2.putText(img1, xy, (x, y), cv2.FONT_HERSHEY_PLAIN,1.0, (0, 0, 0), thickness=1)cv2.imshow("image2", img1)cv2.namedWindow("image1")
cv2.setMouseCallback("image1", on_EVENT_LBUTTONDOWN1)
cv2.imshow("image1", frame)cv2.namedWindow("image2")
cv2.setMouseCallback("image2", on_EVENT_LBUTTONDOWN2)
cv2.imshow("image2", img1)
cv2.waitKey(0)p0=np.float32([[[a[0],b[0]]],[[a[1],b[1]]],[[a[2],b[2]]],[[a[3],b[3]]]])
print(p0)
cv2.destroyAllWindows()

（3）对视频光流进行追踪，p0为当前关键点，p1为视频流中关键点的下一个位置信息

调用cv2.calcOpticalFlowPyrLK()函数进行我们想要的追踪操作

（ps.关注cv2.calcOpticalFlowPyrLK()函数中p0的格式）

import cv2
import numpy as np# feature_params = dict(maxCorners=100, qualityLevel=0.3,
#                       minDistance=7, blockSize=7)
lk_params = dict(winSize=(15,15), maxLevel=2,criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))cap = cv2.VideoCapture('path')
ret, frame = cap.read()      #frame是每一帧的图像scaling_factor = 0.5
frame = cv2.resize(frame, None, fx=scaling_factor,fy=scaling_factor, interpolation=cv2.INTER_AREA)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
p0 = np.float32([[[174,170]],[[180,190]],[[200,200]],[[210,210]]])
# p0 = cv2.goodFeaturesToTrack(gray, mask=None, **feature_params)    #角点检测
print(p0)
mask = np.zeros_like(frame)while True:ret, frame = cap.read()frame = cv2.resize(frame, None, fx=scaling_factor,fy=scaling_factor, interpolation=cv2.INTER_AREA)frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)p1, st, err = cv2.calcOpticalFlowPyrLK(gray, frame_gray, p0, None, **lk_params)print(p1[0][0])
#     print(p0)good_new = p1[st == 1]good_old = p0[st == 1]for i,(new,old) in enumerate(zip(good_new,good_old)):a,b = new.ravel()a=int(a)b=int(b)c,d = old.ravel()c=int(c)d=int(d)cv2.line(mask, (a, b),(c, d),(0, 150, 0), 1)cv2.circle(frame, (a, b), 3, (0, 255, 0), -1)gray = frame_gray.copy()p0 = good_new.reshape(-1, 1, 2)#     img = cv2.add(frame, mask)
#     cv2.imshow("Output", img)k = cv2.waitKey(30)if k == 27:breakcap.release()
cv2.destroyAllWindows()

3、人脸的替换（以为图片的替换为例）

（1）鼠标定点（不再赘述）

（2）矩阵变换

调用cv2.getPerspectiveTransform(), cv2.warpPerspective()函数进行两张图片上的关键点的对比（注：此函数规定对应点为4个点）

（3）内容替换

直接用变换好的图像相应矩阵位置处的值替换原图像位置的值

import numpy as np
import cv2
%matplotlib inlineimg1 = cv2.imread('path1')
img2 = cv2.imread('path2')a =[]
b = []# 一、鼠标定点def on_EVENT_LBUTTONDOWN1(event, x, y, flags, param):# 点击鼠标左键if event == cv2.EVENT_LBUTTONDOWN:xy = "%d,%d" % (x, y)a.append(x)b.append(y)#         cv2.circle(img1, (x, y), 1, (255, 0, 0), thickness=-1)
#         cv2.putText(img1, xy, (x, y), cv2.FONT_HERSHEY_PLAIN,
#                     1.0, (0, 0, 0), thickness=1)
#         cv2.imshow("image1", img1)def on_EVENT_LBUTTONDOWN2(event, x, y, flags, param):# 点击鼠标左键if event == cv2.EVENT_LBUTTONDOWN:xy = "%d,%d" % (x, y)a.append(x)b.append(y)#         cv2.circle(img2, (x, y), 1, (255, 0, 0), thickness=-1)
#         cv2.putText(img2, xy, (x, y), cv2.FONT_HERSHEY_PLAIN,
#                     1.0, (0, 0, 0), thickness=1)
#         cv2.imshow("image2", img2)cv2.namedWindow("image1")
cv2.namedWindow("image2")
cv2.setMouseCallback("image1", on_EVENT_LBUTTONDOWN1)
cv2.setMouseCallback("image2", on_EVENT_LBUTTONDOWN2)
cv2.imshow("image1", img1)
cv2.imshow("image2", img2)
cv2.waitKey(0)# print(a[3],b[3])
# print(a[6],b[6])cv2.destroyAllWindows()# 二、矩阵变换points1 = np.float32([[[a[4],b[4]]],[[a[5],b[5]]],[[a[6],b[6]]],[[a[7],b[7]]]])
points2 = np.float32([[[a[0],b[0]]],[[a[1],b[1]]],[[a[2],b[2]]],[[a[3],b[3]]]])M  =  cv2.getPerspectiveTransform(points1, points2)Affine_img = cv2.warpPerspective(img2, M, (img1.shape[1], img1.shape[0]))# 三、内容替换m=a[2]-a[0]
# n=b[2]-b[0]
for j in range(m):for i in range(m):img1[74+i][125+j] = Affine_img[74+i][125+j]cv2.namedWindow("image3")
cv2.imshow("image3",Affine_img)     # 显示图片
# cv2.imshow("image2",np.array(img_cov, dtype = np.uint8 ))
cv2.waitKey(0)cv2.destroyAllWindows()

四、完整代码

import numpy as np
import cv2
%matplotlib inline#视频第一帧和图像第一帧锁定cap = cv2.VideoCapture('2.mp4')
ret, frame = cap.read()      #frame是每一帧的图像
scaling_factor = 0.5
frame = cv2.resize(frame, None, fx=scaling_factor,fy=scaling_factor, interpolation=cv2.INTER_AREA)
img1 = cv2.imread('face.jpg')a =[]
b = []def on_EVENT_LBUTTONDOWN1(event, x, y, flags, param):# 点击鼠标左键if event == cv2.EVENT_LBUTTONDOWN:xy = "%d,%d" % (x, y)a.append(x)b.append(y)cv2.circle(frame, (x, y), 1, (255, 0, 0), thickness=-1)cv2.putText(frame, xy, (x, y), cv2.FONT_HERSHEY_PLAIN,1.0, (0, 0, 0), thickness=1)cv2.imshow("image1", frame)def on_EVENT_LBUTTONDOWN2(event, x, y, flags, param):# 点击鼠标左键if event == cv2.EVENT_LBUTTONDOWN:xy = "%d,%d" % (x, y)a.append(x)b.append(y)cv2.circle(img1, (x, y), 1, (255, 0, 0), thickness=-1)cv2.putText(img1, xy, (x, y), cv2.FONT_HERSHEY_PLAIN,1.0, (0, 0, 0), thickness=1)cv2.imshow("image2", img1)cv2.namedWindow("image1")
cv2.setMouseCallback("image1", on_EVENT_LBUTTONDOWN1)
cv2.imshow("image1", frame)cv2.namedWindow("image2")
cv2.setMouseCallback("image2", on_EVENT_LBUTTONDOWN2)
cv2.imshow("image2", img1)
cv2.waitKey(0)p0=np.float32([[[a[0],b[0]]],[[a[1],b[1]]],[[a[2],b[2]]],[[a[3],b[3]]]])
print(p0)
cv2.destroyAllWindows()#光流追踪关键点lk_params = dict(winSize=(15,15), maxLevel=2,criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
mask = np.zeros_like(frame)gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)while True:ret, frame = cap.read()frame = cv2.resize(frame, None, fx=scaling_factor,fy=scaling_factor, interpolation=cv2.INTER_AREA)frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)p1, st, err = cv2.calcOpticalFlowPyrLK(gray, frame_gray, p0, None, **lk_params)good_new = p1[st == 1]good_old = p0[st == 1]for i,(new,old) in enumerate(zip(good_new,good_old)):e,f = new.ravel()e=int(e)f=int(f)g,h = old.ravel()g=int(g)h=int(h)cv2.line(mask, (e, f),(g, h),(0, 150, 0), 1)cv2.circle(frame, (e, f), 3, (0, 255, 0), -1)# 二、矩阵变换points1 = np.float32([[[a[4],b[4]]],[[a[5],b[5]]],[[a[6],b[6]]],[[a[7],b[7]]]])points2 = np.float32(p1)M  =  cv2.getPerspectiveTransform(points1, points2)Affine_img = cv2.warpPerspective(img1, M, (frame.shape[1], frame.shape[0]))# 三、内容替换m = p1[2][0][0]-p1[0][0][0]   #m=a[2]-a[0]m=int(m)n = p1[2][0][1]-p1[0][0][1]   #n=b[2]-b[0]n=int(n)a1 = int(p1[0][0][1])a2 = int(p1[0][0][0])a3 = int(p1[0][0][1])a4 = int(p1[0][0][0])for i in range(n):for j in range(m):frame[a1+i][a2+j] = Affine_img[a3+i][a4+j]#     cv2.namedWindow("image3")
#     cv2.imshow("image3",Affine_img)     # 显示图片
#     # cv2.imshow("image2",np.array(img_cov, dtype = np.uint8 ))
#     cv2.waitKey(0)#     cv2.destroyAllWindows()gray = frame_gray.copy()p0 = good_new.reshape(-1, 1, 2)img = cv2.add(frame, mask)cv2.imshow("Output", img)k = cv2.waitKey(30)if k == 27:breakcap.release()
cv2.destroyAllWindows()

完结撒花！

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